Mapping reads to Schistocerca RefSeq genome

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Last updated: 2022-11-01

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Knit directory: locust-phase-transition-RNAseq/

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Load R libraries (install first from CRAN or Bioconductor)

library("knitr")
library("rmdformats")
library("tidyverse")
library("DT")  # for making interactive search table
library("plotly") # for interactive plots
library("ggthemes") # for theme_calc
library("reshape2")

## Global options
options(max.print="10000")
knitr::opts_chunk$set(
    echo = TRUE,
    message = FALSE,
    warning = FALSE,
    cache = FALSE,
    comment = FALSE,
    prompt = FALSE,
    tidy = TRUE
)
opts_knit$set(width=75)

Short Reads mapping and Gene Quantification

STAR only

We used STAR for mapping reads to either 1) their own species reference genome or 2) an alternate sister reference genome. The pipeline is the same, except that the code will change index.

########################################
# Snakefile rule
########################################

#Ahead of the alignment I will build independently the index for STAR, HiSat2 and Segemehl

rule STAR_align:
    input:
        index = REFdir + "/locusts_complete/index_GCF_021461395.2_iqSchAmer2.1_genomic/STAR",
        read1 = OUTdir + "/trimming/{locust}_trim1P_1.fastq.gz",
        read2 = OUTdir + "/trimming/{locust}_trim2P_2.fastq.gz"
    params:
        prefix = OUTdir + "/alignment/STAR/{locust}_"
    output:
        OUTdir + "/alignment/STAR/{locust}_Aligned.sortedByCoord.out.bam"
    shell:
        """
        module load GCC/11.2.0 STAR/2.7.9a
        STAR --runThreadN 8 --genomeDir {input.index} --outSAMtype BAM SortedByCoordinate --quantMode GeneCounts --outFileNamePrefix {params.prefix} --readFilesCommand zcat --readFilesIn {input.read1} {input.read2} 
        """
        
########################################
# Parameters in the cluster.json file
########################################

    "STAR_align":
    {
        "cpus-per-task" : 12,
        "partition" : "medium",
        "ntasks": 1,
        "mem" : "100G",
        "time": "0-08:00:00"
    }

After mapping, we obtained alignment statistics from the *_Log.final.out file and filled out the metadata table with it.

grep 'Number of input reads' *_Log.final.out
grep 'Average input read length' *_Log.final.out
grep 'Uniquely mapped reads number' *_Log.final.out
grep 'Number of reads mapped to multiple loci' *_Log.final.out
grep 'Number of reads mapped to too many loci' *_Log.final.out
grep 'Number of reads unmapped: too many mismatches' *Log.final.out
grep 'Number of reads unmapped: too short' *Log.final.out
grep 'Number of reads unmapped: other' *Log.final.out

STAR only

Quantification using GeneCount

We can note that the option --quantMode GeneCounts from STAR produces the same output as the htseq-count tool if we used the –-sjdbGTFfile option.

In the output file {locust}_ReadsPerGene.out.tab we can obtain the read counts per gene depending if our data is unstranded (column 2) or stranded (columns 3 and 4).

column 1: gene ID
column 2: counts for unstranded RNA-seq.
column 3: counts for the 1st read strand aligned with RNA
column 4: counts for the 2nd read strand aligned with RNA (the most common protocol nowadays)

For our pilot S. gregaria project, we know we used Illumina stranded kit but to check we can with the following code:

grep -v "N_" {locust}_ReadsPerGene.out.tab | awk '{unst+=$2;forw+=$3;rev+=$4}END{print unst,forw,rev}'

#or as a loop
for i in *_ReadsPerGene.out.tab; do echo $i; grep -v "N_" $i | awk '{unst+=$2;forw+=$3;rev+=$4}END{print unst,forw,rev}'; done

In a stranded library preparation protocol, there should be a strong imbalance between number of reads mapped to known genes in forward versus reverse strands. This is what we observe for example on S. cancellata libraries here.

PREFERRED OPTION: We need to extract in our case the 1st and 4th columns for each file.

########################################
# Snakefile rule
########################################

#either ran the following rule
rule reads_count:
        input:
                readtable = OUTdir + "/alignment/STAR2/{locust}_ReadsPerGene.out.tab",
        output:
                OUTdir + "/DESeq2/counts_4thcol/{locust}_counts.txt"
        shell:
                """
                cut -f1,4 {input.readtable} | grep -v "_" > {output}  
                """

#or simply this loop for less core usage
# for i in $SCRATCH/locust_phase/data/alignment/STAR/*ReadsPerGene.out.tab; do echo $i; cut -f1,4 $i | grep -v "_" > $SCRATCH/locust_phase/data/DESeq2/counts_4thcol/`basename $i ReadsPerGene.out.tab`counts.txt; done

ALTERNATIVE OPTION: We can also build a single matrix of expression with all individuals targeted. Below is the example for S. piceifrons:

paste SPICE_*_ReadsPerGene.out.tab | grep -v "_" | awk '{printf "%s\t", $1}{for (i=4;i<=NF;i+=4) printf "%s\t", $i; printf "\n" }' > tmp
sed -e "1igene_name\t$(ls SPICE_*ReadsPerGene.out.tab | tr '\n' '\t' | sed 's/_ReadsPerGene.out.tab//g')" tmp > raw_counts_piceifrons_matrix.txt

STAR and RSEM

STAR and Salmon

HISAT2

Session information

sessionInfo()

FALSE R version 4.2.1 (2022-06-23)
FALSE Platform: x86_64-apple-darwin17.0 (64-bit)
FALSE Running under: macOS Big Sur ... 10.16
FALSE 
FALSE Matrix products: default
FALSE BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
FALSE LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
FALSE 
FALSE locale:
FALSE [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
FALSE 
FALSE attached base packages:
FALSE [1] stats     graphics  grDevices utils     datasets  methods   base     
FALSE 
FALSE other attached packages:
FALSE  [1] reshape2_1.4.4   ggthemes_4.2.4   plotly_4.10.0    DT_0.26         
FALSE  [5] forcats_0.5.2    stringr_1.4.1    dplyr_1.0.10     purrr_0.3.5     
FALSE  [9] readr_2.1.3      tidyr_1.2.1      tibble_3.1.8     ggplot2_3.3.6   
FALSE [13] tidyverse_1.3.2  rmdformats_1.0.4 knitr_1.40       workflowr_1.7.0 
FALSE 
FALSE loaded via a namespace (and not attached):
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